Maybe confusing, but working as expected. When you write: matched_to[np.array([0, 1, 2])] = 3 it calls __setitem__ on matched_to, with arguments (np.array([0, 1, 2]), 3). So numpy understand you want to write 3 at these indices.
When you write: matched_to[:3][match] = 3 it first calls __getitem__ with the slice as argument, which returns a view of your array, then it calls __setitem__ on this view, and it fills your matched_to array at the same time. But when you write: matched_to[np.array([0, 1, 2])][match] = 3 it first calls __getitem__ with the array as argument, which retunrs a *copy* of your array, so that calling __setitem__ on this copy has no effect on your original array. -=- Olivier 2011/8/10 Benjamin Root <ben.r...@ou.edu> > Came across this today when trying to determine what was wrong with my > code: > > import numpy as np > matched_to = np.array([-1] * 5) > in_ellipse = np.array([False, True, True, True, False]) > match = np.array([False, True, True]) > matched_to[in_ellipse][match] = 3 > > I would expect matched_to to now be "array([-1, -1, 3, 3, -1])", but > instead, it is still all -1. > > It would seem that unless the view was created by a slice, then the > assignment into the indexed view would not work as expected. This works: > > >>> matched_to[:3][match] = 3 > > but not: > > >>> matched_to[np.array([0, 1, 2])][match] = 3 > > Note that the following does work: > > >>> matched_to[np.array([0, 1, 2])] = 3 > > Is this a bug, or was I wrong to expect this to work this way? > > Thanks, > Ben Root > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > >
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